Optical and other high-speed communication channels suffer from dispersion which changes the shape of pulses which encode symbols being transmitted. Dispersion and pulse shape changes arise from the fact that different frequency components propagate at different velocities. This phenomenon causes Inter-Symbol Interference (ISI) between neighboring pulses, and ISI limits the number of discrete amplitude levels for symbol pulses which can be successfully detected. Equalization is a way of eliminating or reducing ISI.
If the exact characteristics of the channel are known, ISI can be eliminated or reduced substantially by using a pair of filters, one at the transmitter which does pre-channel equalization, and one at the receiver does post-channel equalization, so as to control the pulse shape distortion. If the filter characteristics of these filters are set correctly, the transmit filter pre-distorts the pulse shapes so that the distortions in the channel do not cause ISI at the sample instants and the receive filter takes care of any remaining ISI noise before each received symbol is fed to the slicer for decision.
However, the characteristics of the channel are rarely known in advance, and are time-varying. In addition, there is always imprecision that arises in implementation of the filters. Therefore, there is always some distortion such that ISI will limit the data rate of the system. To compensate for this residual distortion, equalization is performed, using an equalizer (a type of filter).
In general, equalization at the receiver side is much more popular than pre-equalization at the transmitter side, because it saves the need to inform the transmitter of the exact channel conditions.
Equalizers are adaptive usually to adjust to time varying conditions for ISI reduction. Adaptive Finite Impulse Response (FIR) equalizers are digital tapped delay line filters with impulse responses defined by the tap weights, called the filter coefficients.
The adaptive equalization process involves setting tap weights, decoding data symbols and/or receiving training data and processing it to determine whether slicer errors are occurring or will occur in reception of the data, then altering the tap weights and, sometimes, processing the training data again to determine if the number of errors was reduced. The process of adapting the tap weights to change the filter characteristics continues, until the number of errors at the receiver side is minimized (a convergence state). Typically, adaptation is achieved by observing the error between the desired pulse shape and the actual pulse shape at the output of the equalizer filter, measured at the sampling instants, and then using this error to determine the direction in which the tap weights should be altered to approach an optimum set of values.
Wireless communication systems use a popular approach called training sequence (or pilot sequence) for channel equalizer coefficients setting, where a known signal is transmitted and the channel state is estimated using the combined knowledge of the transmitted and received signal. Generally, the use of training sequences allows reliable and robust tracking of changes in the channel state. Since in wireless communication systems the channel conditions vary rapidly, instantaneous Channel State Information (CSI—known channel properties of a communication link, which needs to be estimated on a short-term basis) also varies rapidly. Therefore, blind equalization (in which there is no available prior knowledge about the channel properties) is not sufficient for wireless systems.
On the other hand, wired communication systems do not use training sequences to estimate distortions in the communication channel, and equalization is mostly based on blind equalization, since changes in the CSI are very slow and there is sufficient time to perform good tracking (of changes). Blind equalization is a digital signal processing technique in which the equalizer coefficients are updated without any knowledge of the specific symbols that were transmitted (except for the symbol constellation) and no knowledge regarding the channel state (except for an initial guess used to compute an initial equalizer). This procedure includes initial equalization of the samples using the initial equalizer, decoding the symbols and using the decoded symbols to improve the equalizer coefficients.
In optical communication, robust tracking requires relatively good channel conditions (at least at the beginning of blind equalization process). However, the channel conditions in optical communication links are relatively very difficult to equalize (due to severe distortions) and therefore, the accuracy and reliability of channel tracking will not be sufficient for effective and robust equalization.
It is therefore an object of the present invention to provide accurate and robust channel tracking technique, for achieving effective and robust equalization of optical communication channels.
Other objects and advantages of the invention will become apparent as the description proceeds.